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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Distributed mixed-integer fuzzy hierarchical programming for municipal solid waste management. Part I: System

Guanhui Cheng1, Guohe Huang2,3, Cong Dong1

  • 1Institute for Energy, Environment and Sustainability Research, University of Regina, Regina, Saskatchewan, S4S 0A2, Canada.

Environmental Science and Pollution Research International
|January 20, 2017
PubMed
Summary
This summary is machine-generated.

Complex municipal solid waste management (MSWM) systems require advanced modeling. This study introduces a novel framework to address MSWM complexities in Beijing, enhancing decision support for sustainable practices.

Keywords:
BeijingDiscretenessHeterogeneitiesHierarchyInteractionsMunicipal solid waste management

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Area of Science:

  • Environmental Science
  • Operations Research
  • Urban Planning

Background:

  • Municipal solid waste management (MSWM) systems face significant challenges due to inherent complexities like heterogeneity, hierarchy, and interactions.
  • Existing MSWM studies, particularly for Beijing, often fail to capture these complexities, limiting reliable decision support.
  • Unaddressed complexities can exacerbate socio-economic and eco-environmental problems, leading to long-term damages.

Purpose of the Study:

  • To develop a novel framework for simulating and optimizing complex MSWM systems.
  • To address the limitations of existing models in reflecting system complexities for better decision-making.
  • To provide a robust tool for analyzing and managing MSWM in a representative case study like Beijing.

Main Methods:

  • Development of a distributed mixed-integer fuzzy hierarchical programming (DMIFHP) framework.
  • Comprehensive analysis of the Beijing MSWM system, including socio-economic, natural, and spatial factors.
  • Discretization of the Beijing MSWM system into 235 grids to represent spatial heterogeneity.
  • Construction of a nonlinear programming model parameterized for the Beijing MSWM system.
  • Proposal of a solution algorithm coupling fuzzy programming and mixed-integer linear programming.

Main Results:

  • A new DMIFHP framework designed to handle MSWM complexities has been successfully developed.
  • The Beijing MSWM system was analyzed and modeled, incorporating spatial heterogeneity through grid discretization.
  • A novel solution algorithm was proposed to scientifically solve the complex DMIFHP model.
  • The study lays the groundwork for optimizing MSWM schemes and understanding underlying mechanisms.

Conclusions:

  • The developed DMIFHP framework offers a significant advancement in modeling complex MSWM systems.
  • This approach provides enhanced capabilities for system simulation and optimization compared to previous methods.
  • The study establishes a foundation for improved decision support in MSWM, particularly in complex urban environments like Beijing.